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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 356 · 40 questions
TalentJR

AI tip (Fair data, fairer AI): Bias usually comes from the training data: if a group is missing or shown in only one way, the model copies that. One-sided data: one age group, only ripe mangoes, only adult voices, only neat handwriting. Balanced data: many ages, colours, voices, regions and styles. Reduce bias: add varied examples, check who is missing, test on many groups, fix wrong labels and listen to complaints.

NameDateTime takenScore ___ / 40
  1. 1.⚖️ One-sided or balanced data: photos of people from only one age group? (a) one-sided data (b) balanced data
  2. 2.Which of these helps reduce bias? (a) test on one group only (b) add more of the same (c) use one group only (d) test on many groups
  3. 3.Which of these helps reduce bias? (a) test on one group only (b) copy old unfair choices (c) balance the data (d) skip testing
  4. 4.True or false: a fruit sorter shown only ripe mangoes is balanced data (fairer for everyone). (a) True (b) False
  5. 5.⚖️ One-sided or balanced data: leaves from every season of the year? (a) one-sided data (b) balanced data
  6. 6.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a fair model (b) a strong battery (c) bias from data (d) a broken screen
  7. 7.🗣️ A voice app understands adults but not children. What is the likely reason? (a) few child voices in data (b) the phone was new (c) it was a rainy day (d) children speak too fast
  8. 8.🔍 Which is the odd one out? (a) test on many groups (b) fix wrong labels (c) copy old unfair choices (d) check who is missing
  9. 9.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) balanced data (b) one-sided data
  10. 10.🔍 Which is the odd one out? (a) ignore complaints (b) check who is missing (c) ask different people (d) balance the data
  11. 11.Which of these helps reduce bias? (a) use one group only (b) add more of the same (c) ignore complaints (d) add varied examples
  12. 12.True or false: “test on one group only” helps reduce bias. (a) False (b) True
  13. 13.⚖️ Where does bias in an AI system usually come from? (a) its training data (b) its colour (c) the weather (d) its screen size
  14. 14.True or false: “ask different people” adds to bias. (a) False (b) True
  15. 15.True or false: a voice app trained only on adult voices is balanced data (fairer for everyone). (a) False (b) True
  16. 16.True or false: “fix wrong labels” helps reduce bias. (a) False (b) True
  17. 17.🛠️ Does this reduce bias or add to it: add more of the same? (a) adds to bias (b) reduces bias
  18. 18.Which of these adds to bias? (a) test on one group only (b) ask different people (c) add varied examples (d) test on many groups
  19. 19.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) one-sided data (b) balanced data
  20. 20.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) balanced data (b) one-sided data
  21. 21.Which of these adds to bias? (a) fix wrong labels (b) test on many groups (c) add more of the same (d) check who is missing
  22. 22.🔍 Which is the odd one out? (a) add more of the same (b) ask different people (c) use one group only (d) copy old unfair choices
  23. 23.🛠️ Does this reduce bias or add to it: ignore complaints? (a) reduces bias (b) adds to bias
  24. 24.🔍 Which is the odd one out? (a) ask different people (b) balance the data (c) add more of the same (d) fix wrong labels
  25. 25.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) False (b) True
  26. 26.🛠️ Does this reduce bias or add to it: use one group only? (a) adds to bias (b) reduces bias
  27. 27.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) reduces bias (b) adds to bias
  28. 28.🛠️ Does this reduce bias or add to it: test on one group only? (a) adds to bias (b) reduces bias
  29. 29.🛠️ Does this reduce bias or add to it: ask different people? (a) reduces bias (b) adds to bias
  30. 30.True or false: leaves from every season of the year is one-sided data (likely to make a biased model). (a) False (b) True
  31. 31.🛠️ Does this reduce bias or add to it: test on many groups? (a) reduces bias (b) adds to bias
  32. 32.🛠️ What helps make an AI system fairer? (a) more varied data (b) one group only (c) ignoring mistakes (d) fewer tests
  33. 33.🛠️ Does this reduce bias or add to it: balance the data? (a) reduces bias (b) adds to bias
  34. 34.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) True (b) False
  35. 35.🔍 Which is the odd one out? (a) add varied examples (b) ask different people (c) skip testing (d) test on many groups
  36. 36.🔍 Which is the odd one out? (a) use one group only (b) add more of the same (c) copy old unfair choices (d) test on many groups
  37. 37.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) one-sided data (b) balanced data
  38. 38.True or false: “add more of the same” adds to bias. (a) True (b) False
  39. 39.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) one-sided data (b) balanced data
  40. 40.Which of these helps reduce bias? (a) ignore complaints (b) check who is missing (c) use one group only (d) add more of the same

Answer key

  1. one-sided data
  2. test on many groups
  3. balance the data
  4. False
  5. balanced data
  6. bias from data
  7. few child voices in data
  8. copy old unfair choices
  9. balanced data
  10. ignore complaints
  11. add varied examples
  12. False
  13. its training data
  14. False
  15. False
  16. True
  17. adds to bias
  18. test on one group only
  19. balanced data
  20. one-sided data
  21. add more of the same
  22. ask different people
  23. adds to bias
  24. add more of the same
  25. False
  26. adds to bias
  27. adds to bias
  28. adds to bias
  29. reduces bias
  30. False
  31. reduces bias
  32. more varied data
  33. reduces bias
  34. False
  35. skip testing
  36. test on many groups
  37. one-sided data
  38. True
  39. balanced data
  40. check who is missing

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